Real-time symptom monitoring using ePROs to prevent adverse events during care transitions

使用 ePRO 进行实时症状监测，以预防护理过渡期间的不良事件

• 批准号: 10345512
• 负责人: Anuj K Dalal
• 金额: $ 40万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10345512
• 关键词: Real; time; symptom; monitoring; using

ABSTRACT Adverse events (AE) during care transitions range from 19-28% and may lead to readmissions, representing an ongoing threat to patient safety. Early identification and escalation of patient-reported symptoms to inpatient and ambulatory clinicians is critical, especially for patients with multiple chronic conditions (MCC). Clinically integrated digital health apps have the potential to more accurately predict post-discharge AEs and improve communication for patients, their caregivers, and the care team. Such tools can provide individualized risk assessments of AEs by systematically collecting relevant patient-reported outcomes (PROs) and leveraging standardized application programming interfaces (API) to combine them with electronic health record (EHR) data. While patient-reported outcomes (PROs) are increasingly used in ambulatory settings, their use for real- time symptom monitoring and escalation during transitions from the hospital is novel and potentially transformative–by both empowering patients to better understand their individualized risks of post-discharge AEs, and improving monitoring while transitioning out of the hospital. Our proposed intervention is grounded in evidence-based frameworks for care transitions, and scaling and spread of digital health tools. To inform our intervention, we propose developing and validating a predictive model of post-discharge AEs for hospitalized MCC patients using relevant PRO questionnaires and electronic health record (EHR) derived variables. We will then combine, adapt, extend, and refine our previously developed EHR-integrated hospital and ambulatory-focused digital health infrastructure to support MCC patients in real-time symptom monitoring using PROs when transitioning out of the hospital. Our intervention uses interoperable, data exchange standards and APIs to seamlessly integrate with existing vendor patient portal offerings, thereby addressing critical gaps and supporting the complete continuum of care. Our multidisciplinary team uses principles of user-centered design and agile software development to rapidly identify, design, develop, refine, and implement requirements from patients and clinicians. Our team will rigorously evaluate this intervention in a large-scale randomized controlled trial in which we compare our real-time symptom monitoring intervention to usual care for patients with MCCs transitioning out of the hospital. Finally, we will conduct a robust mixed methods evaluation to generate new knowledge and best practices for disseminating, implementing, and using this interoperable intervention at similar institutions with different EHR vendors.

抽象的 护理过渡期间的不良事件（AE）范围为19-28％，可能导致再入院，代表 对患者安全的持续威胁。早期识别和升级患者报告的症状到住院 卧床临床医生至关重要，特别是对于具有多种慢性病（MCC）的患者。 集成的数字健康应用程序有可能更准确地预测放电后AES并改善 患者，护理人员和护理团队的沟通。这样的工具可以提供个性化的风险 通过系统地收集相关患者报告的结果（PRO）和利用来评估AES 标准化应用程序界面（API）将它们与电子健康记录（EHR）相结合 数据。尽管患者报告的结果（PRO）越来越多地用于门诊环境中，但它们用于现实 从医院过渡期间的时间症状监测和升级是新颖的，可能是 变革性 - 两者都使患者能够更好地了解他们的个性化病后风险 AES，并在过渡出医院时改善监测。我们提议的干预措施基于 基于循证的护理过渡框架，数字健康工具的扩展和扩展。告知我们 干预，我们建议开发和验证住院后AE的预测模型 MCC患者使用相关的Pro问卷和电子健康记录（EHR）得出的变量。我们 然后，将组合，适应，扩展和完善我们先前开发的EHR集成医院和 以卧床为重点的数字健康基础设施，以实时症状监测支持MCC患者 从医院过渡时使用专业人士。我们的干预使用可互操作的数据交换 标准和API与现有的供应商患者门户产品无缝集成，从而解决 关键的差距和支持完整的护理。我们的多学科团队使用 以用户为中心的设计和敏捷软件开发，以快速识别，设计，开发，完善和 实施患者和临床医生的要求。我们的团队将严格评估此干预措施 大规模随机对照试验，我们将实时症状监测干预与 对于MCC患者过渡的患者通常会护理。最后，我们将进行强大的混合 方法评估以生成新知识和最佳实践，以传播，实施和 在类似的机构与不同的EHR供应商一起使用这种可互操作的干预措施。